Search Results (1,175)

Understanding how urban park landscape features are associated with service population and service radius can inform park planning and management. In Changsha, China, this study measured both indicators for 29 parks on one weekday and one weekend day in spring 2025 using mobile signaling data, quantified landscape features from multisource data, and examined their associations. Service population and service radius were not highly correlated. The adjusted R² values for the two indicators were 71.8% and 84.5%, respectively. Park area and the number of parking lots within the park were significantly associated with both indicators in the same direction. Percent vegetation cover and the number of stores within the park were significantly associated with the two indicators in opposite directions. Elevation, presence of large-scale flower landscapes, distance to the nearest subway station, number of surrounding bus stops, distance to the city center, slope, percent water body, edge density of vegetation patches, number of public restrooms, and number of surrounding residential quarters were significantly associated with only one indicator. These findings reveal synergistic, trade-off, and indicator-specific association patterns between landscape features and urban park recreational services, and may inform differentiated urban park planning and management. Full article

Liquid chromatography–hydrogen/deuterium exchange–mass spectrometry (LC-HDX-MS) provides orthogonal structural information that complements conventional LC-MS and improves confidence in small-molecule structural elucidation. Although HDX-MS is well established in protein research, its application to small molecules remains less developed, and practical guidance for experimental implementation and data interpretation is limited. Here, we evaluate major LC-HDX-MS strategies, including post-column co-infusion, partial HDX, and full HDX approaches, and assess their analytical performance using reference standards and NIST SRM 1950 human plasma. The effects of deuterated mobile phases on background contamination, labeled/unlabeled signal ratios, retention-time shifts, adduct chemistry, and mass spectral interpretation are examined, and currently available software tools for LC-HDX-MS data processing are discussed. Full HDX provided the most complete deuterium incorporation, whereas deuterated mobile phases increased background signals and generally reduced analyte responses. Retention-time shifts were typically small but should be considered when matching features. HDX-derived information provides an additional experimental structural descriptor by defining the number of exchangeable hydrogen atoms and refining adduct assignments, thereby reducing ambiguity during structural annotation. These findings provide practical recommendations for implementing LC-HDX-MS in untargeted LC-MS workflows and demonstrate its value as an orthogonal tool for small-molecule structural elucidation. Full article

To address the need for low-power, real-time underwater acoustic signal processing and autonomous target detection on deep-sea unmanned mobile platforms, such as profiling acoustic buoys and underwater gliders, this study developed an embedded Linux-based signal processing system for buoy platforms. Conventional digital signal processing hardware platforms are often constrained by large size, high power consumption, and limited data communication capability. The proposed system adopts a compact, low-power architecture and a multithreaded processing framework based on the AM6254 heterogeneous multicore processor. It acquires four-channel vector-hydrophone signals together with attitude data from an inertial navigation module and performs band-pass filtering, fast Fourier transform (FFT), direction-of-arrival (DOA) estimation, and constant false alarm rate (CFAR) detection for autonomous target detection. The measured typical power consumption was approximately 2.3 W. Anechoic-tank and sea-trial results showed the lowest tested spectral level at which autonomous detection was achieved was 54 dB at 1 kHz, corresponding to an average in-band level of 46 dB. Under sea state 3, the system maintained continuous bearing tracking after target acquisition for a surface target traveling at 7 kn, up to a range of approximately 7 km, and provided unambiguous bearing estimation. These results demonstrate the target-detection capability and practical applicability of the system under representative operating conditions and indicate its potential for marine environmental monitoring and unmanned-platform observation and detection. Full article

High-mobility dual-functional radar–communication systems require a common waveform that combines delay–Doppler information organization, sensing resolution, and power-efficient transmission. We present a cyclically closed constant-envelope orthogonal time frequency space–continuous phase modulation–linear frequency modulation (OTFS–CPM–LFM) waveform and matched transceiver architecture. Hermitian delay–Doppler mapping and direct-current (DC) row nulling create a real, zero-sum drive with a reversible frame-level phase representation. The communication receiver combines a Tikhonov-regularized waveform inverse with reference-aided unwrapping and tail-biting phase regression, while the radar receiver reconstructs the data-dependent current-frame reference. Numerical results verify the structural waveform properties and characterize communication, radar, and computational tradeoffs. They also quantify degradation under controlled complex-gain channel-state-information mismatch and show that phase regression is less reliable at a low signal-to-noise ratio (SNR). The constant-envelope claim applies only to ideal discrete complex-baseband samples and does not include pulse shaping or radio-frequency hardware. The framework therefore provides a self-consistent waveform interface while exposing tradeoffs among payload, recovery reliability, sensing sidelobes, and implementation cost. Full article

To quantitatively describe changes in skeletal radiopharmaceutical uptake during a training program including swimming in horses with neck and back pain, eighteen showjumping horses with documented vertebral lesions were prospectively included in a training program composed of 4 weeks of ridden work and then 8 weeks combining swimming and ridden exercise. Previously published data on this cohort did not demonstrate any clear change in thoracolumbar mobility. Bone scintigraphy was performed at the 4th and 12th weeks. Radiopharmaceutical uptake was quantified in 205 regions of interest (ROIs) and normalized using a Z-score approach. For each ROI, a linear mixed-effects model was fitted to evaluate the effect of the time point (W4, W12) on the normalized radiopharmaceutical uptake with the horse as a random effect. Overall variation in normalized signal intensity (Z-score difference W12–W04) was small (mean < 0.001; range −0.28 to 0.24). Significant changes between time points were identified in 21 ROIs. Thirteen ROIs located in the axial skeleton showed a decreased radiopharmaceutical uptake, whereas eight ROIs located in the limbs, including the humeral tubercles on both lateral views, showed an increased uptake. During a 2-month training program including swimming, axial skeletal radiopharmaceutical uptake did not increase in this cohort of horses with neck and back pain. However, substantial interindividual variability in scintigraphic changes was observed. Further controlled studies are needed to confirm these preliminary results and to better explore the effects of swimming on horses with axial musculoskeletal disorders. Full article

Background/Objectives: Melioidosis is an environmentally acquired infection caused by Burkholderia pseudomallei (B. pseudomallei). Although historically framed as a tropical disease, evidence indicates that recognized risk can extend beyond classical endemic regions. This narrative review synthesized Digital Object Identifier (DOI)-verified evidence on environmental persistence, climate-sensitive risk, geographic emergence, and One Health preparedness. Methods: Structured narrative searches of PubMed/Medical Literature Analysis and Retrieval System Online (MEDLINE), Europe PubMed Central (Europe PMC), Crossref, and publisher records were conducted for literature available up to 19 June 2026. Forty-four DOI-verified sources were retained. Evidence categories were derived inductively by inferential function during thematic synthesis and used as a qualitative interpretive framework, not as a validated quantitative risk score. Results: B. pseudomallei persists in soil and water, survives nutrient limitation, and clusters in environmental microfoci, but the interpretive value of detection depends on viability, exposure context, and diagnostic endpoint. Rainfall, humidity, flooding, and cyclones are associated with incidence, severity, or mobilization in several settings, supporting climate-sensitive risk rather than uniform geographic spread. Case-based evidence is strongest when it separates importation, local acquisition, environmental establishment, source attribution, and animal sentinel signals. Human risk depends on exposure route, host susceptibility, diagnostic recognition, and access to prolonged antimicrobial management, whereas animal evidence is best interpreted as sentinel or common-exposure evidence unless reservoir or direct-transmission data are available. Conclusions: Melioidosis beyond the tropics requires graded evidence interpretation because environmental detection, modeled suitability, animal signals, and human cases support different levels of geographic and One Health inference; this approach links early signals to surveillance while reserving higher-confidence claims for convergent evidence. Full article

The accurate estimation of real-time passenger flow in urban rail transit (URT) networks plays a crucial role in optimizing the operation and management of urban rail transit systems, with profound implications for daily operation scheduling, passenger flow control, and safety management. Most existing studies have relied solely on a single data source such as Automatic Fare Collection (AFC) data for real-time passenger flow estimation. However, the inherent data uploading delay in the urban rail transit AFC system often leads to the delayed acquisition of passenger flow information. This severely limits the timeliness and accuracy of passenger flow estimation, thereby affecting the efficiency of URT operation and management. To address this critical challenge, this study proposes a multi-source data-driven estimation model to achieve the fusion of multi-source heterogeneous data covering the uploaded AFC data, historical passenger flow data and mobile phone signaling data collected from mainstream sensors such as through-beam photoelectric sensors and RFID/NFC sensors. In the proposed model, various types of information are taken into account by extracting features of the different data sources. The advantages of the proposed model are validated by utilizing multi-source data from Chengdu, China. The experimental results demonstrate that the proposed model achieves higher accuracy compared to existing benchmark models. Compared with the second-best-performing model, it reduces the MAE by 8.1%, RMSE by 10.7%, and MAPE by 17.2% at the 15 min time granularity, which shows that the proposed model has effective performance in terms of accuracy and stability. Full article

Next-generation wireless systems are becoming increasingly complex, and there is a growing need for intelligent mobility management mechanisms that can ensure service continuity while making efficient use of network resources. In 5G and future 6G networks, dense small-cell deployments, heterogeneous architectures, and highly mobile users mean that frequent handovers (HOs), uneven traffic distribution, and variable network conditions often lead to degraded user experience, higher signalling overhead, and inefficient use of resources. Because user movement continuously redistributes traffic across cells, effective mobility management is inseparable from load balancing, and the HO process serves as the primary mechanism through which the network manages both. Recent advances in artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), offer an opportunity to transform mobility management from reactive to predictive, since data-driven solutions can forecast user movement, fine-tune HO execution, and dynamically allocate radio resources. This paper presents a comprehensive survey of AI-enabled mobility management strategies for 5G, Beyond 5G, and upcoming 6G networks, with particular attention to HO optimization and load balancing. The surveyed literature is organized around the complete lifecycle of AI-enabled mobility management, from mobility prediction and HO decision-making through parameter optimization and execution to KPI monitoring and model updating. This structure is used to classify existing frameworks according to their architectures, learning approaches, and optimization goals. The survey then examines how intelligent HO schemes address critical issues such as load balancing, interference mitigation, connection reliability, and quality-of-service maintenance, and compares conventional and AI-based methods against standardized key performance indicators for mobility robustness, resource efficiency, and service continuity. Finally, the paper discusses unresolved problems and emerging trends, including federated learning, multi-connectivity, and non-terrestrial integration, that will shape the evolution of autonomous mobility management solutions for future wireless networks. Full article

Brassica crops, encompassing globally important vegetables and oilseeds, face severe threats from diverse biotic and abiotic stresses. Plant secondary metabolites constitute the chemical foundation of defence, and metabolomics has emerged as an effective systems biology tool for comprehensively dissecting stress-induced metabolic changes. Recent progress in applying metabolomics to elucidate defence mechanisms in Brassica crops is systematically synthesised here. Major stresses confronting Brassica crop production and the metabolic basis of plant defence are first outlined. Current analytical platforms, including liquid chromatography–mass spectrometry, gas chromatography–mass spectrometry, ion mobility spectrometry, and mass spectrometry imaging, are critically evaluated alongside data processing workflows and multi-omics integration strategies. Key defence-related metabolite classes identified in Brassica crops, notably glucosinolates (GSLs) and their hydrolysis products, phenolic compounds, and lipid-derived signalling molecules, are surveyed with emphasis on their respective functions in biotic and abiotic stress responses. Metabolomics has been instrumental in revealing distinct metabolic reprogramming patterns triggered by diverse stresses, including pathogen infection, insect herbivory, drought, salinity, temperature extremes, and heavy metal stress. Metabolomics-informed crop improvement strategies, including marker-assisted breeding, genetic and metabolic engineering, and precision agronomic practices, are discussed together with current technical bottlenecks and future directions involving artificial intelligence, metabolic modelling, and spatial metabolomics. The compiled knowledge provides a comprehensive reference for leveraging metabolomics to enhance stress resilience and sustainable production of Brassica crops. Full article

Improving the performance of computer networks using seamless Wi-Fi can be achieved by implementing a number of strategies and technologies. Strategies include, first of all, the optimal location of routers and access points, the use of a multi-band network or routers supporting different bands. Routers with support for beamforming technology, which directs the Wi-Fi signal directly to connected devices, allow you to improve the signal quality and data transfer speed. Increasing the performance of Wi-Fi computer networks is also provided by the use of network monitoring and management software, which allows you to monitor its performance and respond to possible problems in the network infrastructure. This is an important task, because it determines the quality and convenience of access to network resources. First of all, it allows you to achieve a high data transfer rate, which is especially important in conditions of high traffic necessary for demanding applications. Seamless Wi-Fi technologies also promote increased mobility and flexibility of users, allowing them to connect to the network in any place with a good signal without having to use wired connections. Network management becomes more efficient with automatic switching between access points and increased fault tolerance in the face of changing traffic usage scales. Quantitative results: Implementation of the Wi-Fi roaming mechanism using the IEEE 802.11 specification; Wi-Fi performance measurements obtained for various IEEE 802.11n HT20 and IEEE 802.11a client ratios; the original test environment included 50 laptops and netbooks from various manufacturers, equipped with various operating systems and wireless network adapters; seamless Wi-Fi technologies based on IEEE 802.11k, IEEE 802.11v, and IEEE 802.11r improve communication continuity during device mobility and support real-time AI-based decision making; Wi-Fi based on local communication standards (WLAN-Wireless Local Area Network). It allows data transmission speeds from 1 Mb∙s⁻¹ to 6.75 Gb∙s⁻¹. Indoors, the Wi-Fi range is 20 m, and outdoors 100 m; WiMax (Worldwide Interoperability for Microwave Access) is a built-in set of wireless broadband standards that provide a constant data rate of 1 Gb∙s⁻¹ and 100 Mb∙s⁻¹ in a cellular network; LR-WPANs (Low-Rate Wireless Personal Area Networks) are standards that are the basis for higher communication protocols, ZigBee. They offer data rates ranging from 40 kb to 250 kb∙s⁻¹. In devices with limited resources, these standards operate at 2.4 GHz at higher transmission speeds and 868/915 MHz at lower. The novelty in the article is the implementation of the Wi-Fi roaming mechanism, presentation of Wi-Fi scenarios, discussion of module generations, indication of integrated agriculture in terms of modern digitalization technologies, and characteristics of smart farming. Full article

The growing complexity of urban mobility requires datasets that integrate dynamic traffic observations with meteorological, geometric, and urban-context information. This study presents MUTra-CDMX, a multisource urban traffic dataset covering a 14.72 km section of the Insurgentes Sur corridor in Mexico City. Traffic data were obtained from TomTom at five-minute intervals for 20 consecutive road segments from 1 November 2024 to 28 February 2025. Hourly meteorological data were retrieved from Meteosource, while segment-level geometry, topology, signalized locations, and nearby points of interest were derived from TomTom metadata and OpenStreetMap. The primary analytical file contains 691,200 segment–timestamp records and 12 variables describing traffic and free-flow conditions, meteorological information, derived operational indicators, and reconstruction status. Of these records, 682,264 are original observations and 8936 are reconstructed segment–timestamp combinations, identified by the Boolean variable is_imputed. Technical validation confirmed complete temporal coverage, preservation of original traffic observations, consistent weather alignment, and reconstruction performance through artificial masking. Predictive utility was evaluated through chronological travel-time forecasting under a leakage-controlled protocol. At the 30 min horizon, XGBoost achieved a mean absolute error of 12.84 s, a root mean squared error of 37.91 s, and a coefficient of determination ($R^2$) of 0.771, outperforming a persistence baseline. MUTra-CDMX supports congestion analysis, imputation studies, spatiotemporal modeling, and travel-time forecasting. Full article

Reproducible electrode placement remains a critical, unresolved challenge in wearable ECG systems, where manual electrode attachment introduces inter-session positional error that degrades signal morphology and compromises multi-lead representation. Although the 12-lead clinical ECG system is the gold standard, conventional setups are often bulky, wired, and dependent on operator expertise, limiting their use in prehospital or remote care. Recent advancements in wearable and wireless ECG systems have improved mobility and real-time monitoring, but they typically suffer from limited lead coverage, discomfort, and unstable connectivity. This paper introduces BioShield-12, an anatomically adaptive thermoplastic polyurethane (TPU) shield fabricated using fused deposition modeling (FDM) to improve multi-site electrode placement and 12-lead electrocardiogram (ECG) reconstruction from a single-shield design based on anthropometric data from ten healthy adults (five male, five female; sizing cohort). Mechanical characterization confirmed TPU’s suitability as a compliant wearable substrate, with toughness of 34.4 MJ/m³ and elastic modulus of 79.1 MPa. Feasibility validation against a research-grade reference (BIOPAC MP36) in twenty healthy subjects (n = 20; 10 male, 10 female; mean age 21 ± 4 years) demonstrated consistent signal fidelity (SNR: 17.5–22.5 dB; Pearson r: 0.93–0.98; power-line interference ratio (PLIR): 1.0–3.2 × 10⁻⁵) and confirmed feasibility of 12-lead reconstruction. This work provides proof-of-concept for an additive manufacturing-based conformal electrode interface that eliminates variability in placement without needing individual attachment. Full article

The integration of low-Earth-orbit (LEO) satellites with unmanned aerial vehicles (UAVs) promises high-throughput and flexible wireless connectivity, yet it faces critical challenges in simultaneously guaranteeing data rates and long-term energy harvesting under mobility and imperfect channel state information (CSI). Additionally, the rate–energy trade-off imposed by simultaneous wireless information and power transfer (SWIPT) further complicates per-slot resource allocation. In this paper, we propose a Lyapunov-based scheduling framework that stabilizes UAV data and virtual energy queues while maximizing weighted throughput. The framework employs a custom inner solver combining successive convex approximation (SCA) and weighted minimum mean-square error (WMMSE) optimization to efficiently compute per-slot beamformers and power-splitting ratios. Our approach explicitly accounts for UAV mobility, Rician fading channels with Doppler, and circuit nonlinearities in energy harvesting, ensuring feasible and energy-aware SWIPT operation. A LEO satellite–UAV integrated communication system is considered, where multiple satellites provide wireless connectivity to energy-constrained UAVs operating in a dynamic three-dimensional environment. The satellites employ multi-antenna transmission, while the UAVs rely on energy harvesting mechanisms to sustain their operation. The communication links are characterized by dominant line-of-sight propagation conditions, and UAV trajectories are adaptively optimized to improve network performance and energy efficiency. Simulation results demonstrate that the proposed Lyapunov-based SCA-WMMSE framework significantly outperforms a fixed baseline approach, providing substantial improvements in signal quality, achievable data rates, and harvested energy. Moreover, the proposed method maintains stable energy management behavior and guarantees long-term energy sustainability for the UAVs. Full article

Accurate estimation of Alzheimer’s disease (AD) severity from structural magnetic resonance imaging (MRI) remains difficult, as disease-associated anatomical alterations are often subtle and publicly available datasets are typically too small to support robust deep learning model training. This feasibility study sought to determine how much Alzheimer’s disease spectrum-related information could be extracted from a small structural MRI cohort using a deliberately lightweight two-dimensional convolutional neural network (2D CNN), and whether transfer learning improves model performance. This study was intended as a methodological proof of concept rather than the development of a clinically deployable diagnostic tool. Structural scans and Clinical Dementia Rating (CDR) labels from the OASIS-1 dataset were filtered to 214 subjects: 124 cognitively normal (CN), 65 with mild cognitive impairment (MCI; CDR = 0.5), and 25 with AD-level impairment (CDR ≥ 1). A compact 2D CNN trained from scratch and a transfer learning model (frozen ImageNet MobileNetV2 features) were evaluated on four binary tasks (CN vs. AD, MCI vs. AD, CN vs. MCI, and CN vs. any impairment) under identical pre-processing and subject-level repeated 5-fold cross-validation (10 repeats), with the decision threshold tuned only on an inner split. Discrimination was summarized by ROC-AUC with 95% confidence intervals (CIs), permutation tests against chance, and per-task sensitivity and specificity. The from-scratch CNN recovered only a broad normal-versus-impaired signal (CN vs. any impairment AUC 0.59) and was at chance on adjacent-stage tasks (MCI vs. AD 0.41; CN vs. MCI 0.51). Transfer learning improved every task: CN vs. AD AUC 0.745 (95% CI 0.730–0.763), CN vs. any impairment 0.642, CN vs. MCI 0.601, and MCI vs. AD 0.599. On an independent OASIS-2 cohort, the transfer learning CN vs. AD model retained AUC 0.748. In this small-data regime, transfer learning recovers substantially more Alzheimer-spectrum signals than a from-scratch CNN, but performance remains modest because it is bounded by CDR-based, non-biomarker-confirmed labels, suggesting the model separates CDR-defined cognitive-status groups rather than detecting AD pathology. Full article

Personalized language learning in digital education faces challenges in balancing personalization, data privacy, and limited user interaction data. To mitigate these issues, this paper proposes PERFED, a Privacy-Preserving Personalized Federated Learning paradigm with Reinforcement Learning and Meta-Learning for adaptive digital language education. PERFED introduces three key innovations: (i) a privacy-preserving federated optimization mechanism with adaptive differential privacy noise that adjusts based on client data sensitivity to ensure strong privacy protection while minimizing utility loss; (ii) a reinforcement learning-based adaptive policy that dynamically selects personalized learning strategies to improve learner engagement and accelerate convergence; and (iii) a meta-learning-driven module designed for quick adaptation to new users in low-data scenarios, improving cold-start performance in personalized language learning scenarios. Experiments on the archived multilingual Duolingo Second Language Acquisition Modeling corpus show that PERFED achieves $83.3\%\pm 0.47$ accuracy over five runs, outperforming FedAvg (78.5%), FedProx (80.2%), and FedPer (81.9%) under non-IID federated settings. PERFED also reduces convergence rounds while improving training stability under heterogeneous client distributions, all while maintaining a maximum cumulative privacy loss of $(ϵ=4.84, \delta ={10}^{-5})$ under a Rényi differential privacy accountant. These results demonstrate the effectiveness and robustness of PERFED for privacy-preserving intelligent language education. The evaluation is an offline synchronous simulation; the behavioral engagement signal is a log-derived proxy rather than a human-subject measure, and mobile deployment performance is not claimed. Full article